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  1.  8
    Geomechanical Analysis of Microseismicity in an Organic Shale: A West Virginia Marcellus Shale Example.Erich Zorn, Abhash Kumar, William Harbert & Richard Hammack - 2019 - Interpretation 7 (1):T231-T239.
    Using an innovative workflow incorporating microseismic attributes and geomechanical well logs, we have defined major geomechanical drivers of microseismic expression to understand reservoir stimulation response in an engineering/geologic context. We sampled microseismic data from two hydraulically fractured Marcellus wells in the Appalachian Basin, West Virginia, vertically through the event cloud, crossing shale, limestone, sandstone, and chert. We focused our analysis on the Devonian organic shale and created pseudologs of moment magnitude Mw, b-value, and event count. The vertical moving-average sampling of (...)
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  2.  10
    Integrating Distributed Acoustic Sensing, Borehole 3C Geophone Array, and Surface Seismic Array Data to Identify Long-Period Long-Duration Seismic Events During Stimulation of a Marcellus Shale Gas Reservoir.Payam Kavousi Ghahfarokhi, Thomas H. Wilson, Timothy Robert Carr, Abhash Kumar, Richard Hammack & Haibin Di - 2019 - Interpretation 7 (1):SA1-SA10.
    Microseismic monitoring by downhole geophones, surface seismic, fiber-optic distributed acoustic sensing, and distributed temperature sensing observations were made during the hydraulic fracture stimulation of the MIP-3H well in the Marcellus Shale in northern West Virginia. DAS and DTS data measure the fiber strain and temperature, respectively, along a fiber-optic cable cemented behind the casing of the well. The presence of long-period long-duration events is evaluated in the borehole geophones, DAS data, and surface seismic data of one of the MIP-3H stimulated (...)
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    Evaluating Proxies for the Drivers of Natural Gas Productivity Using Machine-Learning Models.William Harbert, Richard Hammack, Erich Zorn, Alexander Bear, Timothy Carr & Abhash Kumar - 2021 - Interpretation 9 (4):SG31-SG46.
    The extensive development of unconventional reservoirs using horizontal drilling and multistage hydraulic fracturing has generated large volumes of reservoir characterization and production data. The analysis of this abundant data using statistical methods and advanced machine-learning techniques can provide data-driven insights into well performance. Most predictive modeling studies have focused on the impact that different well completion and stimulation strategies have on well production but have not fully exploited the available in situ rock property data to determine its role in reservoir (...)
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